Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

The impacts of value chain upgrading on the export of processed food

Upgrading in global value chains (GVCs) is an important path for developing countries to move along to capture higher benefits. Several qualitative studies of GVCs have identified two main upgrading types: economic (product, process, functional, and inter-sectoral) and social upgrading. The upgrading concept is widespread in productive sectors such as processed food, which has become a key export for developing countries. However, they have confronted multiple product standards required by global buyers. This study uses a case study of Thailand’s processed food exports (TPFEs). Although Thailand is a leading exporter of processed food, the country needs to upgrade in many areas related to production to meet global product standards and requirements. If Thailand fails to comply with global product standards, it will lose its export competitiveness. This study uses a gravity model to evaluate the impact of economic and social upgrading (EUP and SUP) on TPFEs. Our results show that upgrading types are significant in TPFEs, particularly for exports to developed countries. Process upgrading has a negative impact on TPFEs because of increased production costs to comply with product standards. However, process upgrading can lead to increased producers’ and exporters’ knowledge about how to comply with international standards. Consequently, process upgrading exhibits a lagged positive effect on TPFEs.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

跨越竞争丛林与范式重构：全球治理改革进程中的疫苗产业创新治理

疫苗作为新冠疫情防控的最关键手段之一，已在各国抗疫中发挥积极影响。疫苗研发、生产、注射意愿以及全球分配的公平性与可及性等一系列问题日益受到世界关注。分析全球治理改革中，疫苗作为一种全球公共产品的创新治理问题，讨论疫苗产业发展特征与创新规律演化，剖析西方国家疫苗产业创新治理困境以及“竞争丛林”逻辑，阐释中国行动方案及对全球治理改革的贡献。最终提出疫苗产业创新治理机制对推动全球治理改革的政策含义。     

Cross-border exchanges and volatility forecasting

We test for the performance of a series of volatility forecasting models (GARCH 1,1; EGARCH 1,1; CGARCH) in the context of several indices from the two oldest cross-border exchanges (Euronext; OMX). Our findings overall indicate that the EGARCH (1,1) model outperforms the other two, both before and after the outbreak of the global financial crisis. Controlling for the presence of feedback traders, the accuracy of the EGARCH (1,1) model is not affected, something further confirmed for both the pre and post crisis periods. Overall, ARCH effects can be found in the Euronext and OMX indices, with our results further indicating the presence of significant positive feedback trading in several of our tests.     

The saving–investment correlation puzzle is still a puzzle

A number of simulation studies claim to have solved the Feldstein–Horioka puzzle by demonstrating that a high time-series correlation between saving and investment naturally arises from business cycle shocks. This paper uses panel data of saving and investment controlled for business cycle shocks to empirically test the significance of cyclical shocks — productivity, fiscal and the terms of trade shocks — in explaining a high saving–investment correlation. The estimation results reveal that conventional aggregate shocks only partially explain the high saving–investment correlation. Moreover, country differences in the size of the GNP and the non-traded sector do not significantly affect the saving–investment correlation. The saving–investment correlation puzzle remains a puzzle after all.     

公开市场操作与利率期限结构行为——基于混频数据信息的研究视角

在混频数据信息环境中,精准识别公开市场操作(央行政策利率)和国债收益率曲线(基准利率体系)之间的关联机制至关重要,其影响了货币政策期限结构传导的有效性。本文在混频Nelson-Siegel(N-S)利率期限结构模型框架下,引入央行政策利率,揭示公开市场操作与利率期限结构(水平、斜率、曲度)因子之间的作用机制。实证结果表明:混频数据信息条件下,引入的公开市场操作信息显著改进国债收益率曲线的拟合效果;斜率因子冲击对公开市场操作具有显著的正向影响,而利率期限结构因子对政策调控的反应不敏感。进一步研究表明,2015年以来,公开市场操作对斜率因子的影响逐渐扩大,政策利率向国债收益率曲线的传导效率得到显著提高,我国现代货币政策框架日益健全。     

发展特许连锁经营,促进城乡居民就业

本依据我国近年来特许连锁经营发展的形势，阐述了特许连锁经营在民间投资和就业发展上的特有功效，提出了利用特许连锁经营促进我国城乡就业的观点，并指出了需要进一步完善的政策和措施。     

气候变化对经济金融系统性冲击的物理风险研究

中国作为全球最大的发展中国家,其综合实力伴随着经济、科技的快速发展而不断增强。但我国的极端气候事件趋多趋强,气候风险水平呈上升趋势,影响我国经济社会的发展。金融作为经济运行的核心,在宏观经济中有着举足轻重的作用。为了研究气候变化与经济金融稳定之间的关系,本文对极端气候事件损失体系和区域经济金融稳定性综合体系进行指数合成并构建面板模型探讨气候变化中气温和降水量因素的变动和极端气候事件对区域金融稳定的影响,得出气候变化的物理风险通过极端气候事件对经济发展和金融稳定有负面影响,并根据实证结果提出完善绿色金融市场和优化信息披露制度的政策建议。